{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    " <font size=\"5\" > **基于机器学习的工程机械设备故障预测系统Part1 - EDA 部分**</font>\n",
    "</div>\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在机器学习数据挖掘火热的现在,机械制造等行业也希望将机器学习等新的技术融入到这些行业当中,希望通过机器学习等算法来判断出机械设备生产中的故障,减少人工专家的检测节省成本，提高生产效率。\n",
    "\n",
    "本次的赛题也是基于这样一个背景,因为好奇也因为是南京市政府的比赛,所以我当时也就参加了。赛题的链接如下,对这一块感兴趣的可以去赛题主页去查看详情,此次我主要分析当时我们团队的一个解题思路,我们团队在当时A榜拿到两次第一,第一次是纯特征工程+算法拿到的第一;第二次是通过探索测试集发现测试集leak直接拿到了线上的满分,当然,后来因为这个原因又加入了复赛，具体细节就不再阐述。\n",
    "\n",
    "该赛题最为核心的一个部分就是eda,通过简单的eda部分就可以直接设计简单的规则德大测试集中的一些真实label。而至于如何通过观察测试集来直接拿到满分,这个我们将会在Part2部分介绍。\n",
    "\n",
    "\n",
    "- 赛题链接:https://www.kesci.com/home/competition/5b35f9ed6b2f551eb1e3d528\n",
    "\n",
    "\n",
    "我们将该比赛的方案分解为下面的两大模块进行阐述,\n",
    "- 数据探索分析：EDA(Exploratory data analysis)\n",
    "- 算法建模：Model(Modeling) \n",
    "\n",
    "在本篇Notebook中，我们EDA的介绍,eda做好了该赛题基本也就进入前十了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#赛题背景\" data-toc-modified-id=\"赛题背景-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>赛题背景</a></span></li><li><span><a href=\"#工具包导入-&amp;-数据读取\" data-toc-modified-id=\"工具包导入-&amp;-数据读取-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>工具包导入 &amp; 数据读取</a></span><ul class=\"toc-item\"><li><span><a href=\"#工具包导入\" data-toc-modified-id=\"工具包导入-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>工具包导入</a></span></li><li><span><a href=\"#读取数据\" data-toc-modified-id=\"读取数据-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>读取数据</a></span></li></ul></li><li><span><a href=\"#数据EDA\" data-toc-modified-id=\"数据EDA-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>数据EDA</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据集分析\" data-toc-modified-id=\"数据集分析-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>数据集分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#缺失值分析\" data-toc-modified-id=\"缺失值分析-3.1.1\"><span class=\"toc-item-num\">3.1.1&nbsp;&nbsp;</span>缺失值分析</a></span></li><li><span><a href=\"#单值分析\" data-toc-modified-id=\"单值分析-3.1.2\"><span class=\"toc-item-num\">3.1.2&nbsp;&nbsp;</span>单值分析</a></span></li><li><span><a href=\"#Label转换-&amp;-分析\" data-toc-modified-id=\"Label转换-&amp;-分析-3.1.3\"><span class=\"toc-item-num\">3.1.3&nbsp;&nbsp;</span>Label转换 &amp; 分析</a></span></li><li><span><a href=\"#数据再次清洗\" data-toc-modified-id=\"数据再次清洗-3.1.4\"><span class=\"toc-item-num\">3.1.4&nbsp;&nbsp;</span>数据再次清洗</a></span></li><li><span><a href=\"#单元变量分析\" data-toc-modified-id=\"单元变量分析-3.1.5\"><span class=\"toc-item-num\">3.1.5&nbsp;&nbsp;</span>单元变量分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#ACCRUNTIMES（油门当前值(%)）\" data-toc-modified-id=\"ACCRUNTIMES（油门当前值(%)）-3.1.5.1\"><span class=\"toc-item-num\">3.1.5.1&nbsp;&nbsp;</span>ACCRUNTIMES（油门当前值(%)）</a></span></li><li><span><a href=\"#READFLAG\" data-toc-modified-id=\"READFLAG-3.1.5.2\"><span class=\"toc-item-num\">3.1.5.2&nbsp;&nbsp;</span>READFLAG</a></span></li><li><span><a href=\"#FRID（自动编号）\" data-toc-modified-id=\"FRID（自动编号）-3.1.5.3\"><span class=\"toc-item-num\">3.1.5.3&nbsp;&nbsp;</span>FRID（自动编号）</a></span></li><li><span><a href=\"#PROVINCE\" data-toc-modified-id=\"PROVINCE-3.1.5.4\"><span class=\"toc-item-num\">3.1.5.4&nbsp;&nbsp;</span>PROVINCE</a></span></li><li><span><a href=\"#AREA\" data-toc-modified-id=\"AREA-3.1.5.5\"><span class=\"toc-item-num\">3.1.5.5&nbsp;&nbsp;</span>AREA</a></span></li><li><span><a href=\"#COLLECTTIME-&amp;-FAULTTIME\" data-toc-modified-id=\"COLLECTTIME-&amp;-FAULTTIME-3.1.5.6\"><span class=\"toc-item-num\">3.1.5.6&nbsp;&nbsp;</span>COLLECTTIME &amp; FAULTTIME</a></span></li><li><span><a href=\"#UNIQUENO\" data-toc-modified-id=\"UNIQUENO-3.1.5.7\"><span class=\"toc-item-num\">3.1.5.7&nbsp;&nbsp;</span>UNIQUENO</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#小结\" data-toc-modified-id=\"小结-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>小结</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 赛题背景\n",
    "\n",
    "随着传感、通信、云计算等技术的发展，必然导致工程机械行业向信息化、智能化方向大步前进，从而提高工程机械的高科技含量，满足不同客户的需求，促进工程机械的繁荣发展。 由于工程施工技术的不断发展完善，用户对工程机械设备也提出了更高的市场要求，实现工程机械工作过程的在线状态监测和自动故障诊断，预测并根据一定的智能体系排除可能出现的故障是目前工程机械行业的最新发展动向。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#  工具包导入 & 数据读取\n",
    "##  工具包导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/zjpy36/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import lightgbm as lgb \n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import f1_score\n",
    "import warnings\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('/data/NJGY/train/dataset.csv')\n",
    "test = pd.read_csv('/data/NJGY/test/testing_set_for_users.csv')\n",
    "train.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "dbd_faultcode = pd.read_csv('/data/NJGY/train/dbd_faultcode.csv',encoding='gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据EDA\n",
    "在对数据进行分析的时候，我们分别对训练集，测试集进行单独分析，然后再将训练集和测试集结合起来分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集分析\n",
    "我们发现\n",
    "- 训练集中共有124列数据;\n",
    "- 数据集中包含浮点数，整数以及字符串等三种类型，属于混合类的数据。\n",
    "\n",
    "所以在后续进行分析的时候，我们分别从不同的数据类型出发对我们的数据进行分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ACCEXCCOUNT</th>\n",
       "      <th>ACCRUNTIMES</th>\n",
       "      <th>ACCSTATE</th>\n",
       "      <th>ACCWORKTIMES</th>\n",
       "      <th>ALARM_0001</th>\n",
       "      <th>ALARM_0002</th>\n",
       "      <th>ALARM_0003</th>\n",
       "      <th>ALARM_0004</th>\n",
       "      <th>ALARM_0005</th>\n",
       "      <th>ALARM_0006</th>\n",
       "      <th>...</th>\n",
       "      <th>VEHICLENO</th>\n",
       "      <th>VEHICLESTATUS</th>\n",
       "      <th>VERSIONNUM</th>\n",
       "      <th>WATERTEMPERATURE</th>\n",
       "      <th>WIRELESSTEMP1</th>\n",
       "      <th>WIRELESSTEMP2</th>\n",
       "      <th>WIRELESSTEMP3</th>\n",
       "      <th>WIRELESSTEMP4</th>\n",
       "      <th>WORKDETAILID</th>\n",
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       "    </tr>\n",
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       "      <td>NaN</td>\n",
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       "      <td>8396881.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>XUG02233HHJE00797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2172359.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1730.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 124 columns</p>\n",
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      "text/plain": [
       "   ACCEXCCOUNT  ACCRUNTIMES  ACCSTATE  ACCWORKTIMES  ALARM_0001  ALARM_0002  \\\n",
       "0          NaN          NaN       1.0       1095.83         0.0         0.0   \n",
       "1          NaN          NaN       1.0       1377.16         0.0         0.0   \n",
       "2          NaN          NaN       0.0        261.22         0.0         0.0   \n",
       "3          NaN          NaN       1.0       1730.52         0.0         0.0   \n",
       "4          NaN          NaN       1.0          0.05         0.0         0.0   \n",
       "\n",
       "   ALARM_0003  ALARM_0004  ALARM_0005  ALARM_0006    ...     \\\n",
       "0         0.0         0.0         0.0         0.0    ...      \n",
       "1         0.0         0.0         0.0         0.0    ...      \n",
       "2         0.0         0.0         0.0         0.0    ...      \n",
       "3         0.0         0.0         0.0         0.0    ...      \n",
       "4         0.0         0.0         0.0         0.0    ...      \n",
       "\n",
       "           VEHICLENO  VEHICLESTATUS  VERSIONNUM  WATERTEMPERATURE  \\\n",
       "0  XUG02631THJE00230            1.0         NaN               NaN   \n",
       "1  XUG02633TGJE00499            1.0         NaN               NaN   \n",
       "2  XUG02233HHJE00797            0.0         NaN               NaN   \n",
       "3  XUG02233VHJE01038            1.0         NaN               NaN   \n",
       "4  XUG02233CHJE00883            1.0         NaN               NaN   \n",
       "\n",
       "   WIRELESSTEMP1  WIRELESSTEMP2  WIRELESSTEMP3  WIRELESSTEMP4  WORKDETAILID  \\\n",
       "0            NaN            NaN            NaN            NaN     7285148.0   \n",
       "1            NaN            NaN            NaN            NaN     8396881.0   \n",
       "2            NaN            NaN            NaN            NaN     2172359.0   \n",
       "3            NaN            NaN            NaN            NaN     2576101.0   \n",
       "4            NaN            NaN            NaN            NaN     5953890.0   \n",
       "\n",
       "  WORKHOURS  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1875088 entries, 0 to 1875087\n",
      "Columns: 124 entries, ACCEXCCOUNT to WORKHOURS\n",
      "dtypes: float64(118), int64(1), object(5)\n",
      "memory usage: 1.7+ GB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值分析\n",
    "\n",
    "首先我们先对数据进行简单的缺失值分析，先分析数据缺失的情况，将数据分为严重缺失，较严重缺失，部分缺失和不缺失处理。其中：\n",
    "- 严重缺失（<font color=\"red\">缺失率达到99%以上</font>）的数据统计意义不大，所以我们往往直接将其删除；\n",
    "- 较为严重缺失的数据（<font color=\"red\">达到95%以上</font>），我们先分析不缺失的数据，因为此类数据可能隐藏某些重要的信息，我们对其进行与标签的相关性分析，然后再考虑是否将其删除；\n",
    "- 部分缺失和不缺失的数据往往带有较为重要的信息，我们暂时将其保留。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "all_missing_columns = train.columns[train.isnull().sum() == train.shape[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 在124列的训练集特征中，我们的数据有32列数据是完全缺失的。\n",
    "> 这些数据对于模型的训练无法带来任何增益，所以我们选择将其直接删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共有 32 个特征列缺失，\n",
      "这些特征列为:  Index(['ACCEXCCOUNT', 'ENGINELOADSTATE', 'ENVHUMIDITY', 'ENVTEMPERATURE',\n",
      "       'INSIDE_HUMIDITY', 'INSIDE_TEMPERATURE', 'ISANALYSED', 'MILENUMBER',\n",
      "       'MIXSTATE', 'OC', 'OILLOCATION', 'OILPERCENT1', 'OILPRESSURE',\n",
      "       'OILTEMPERATURE', 'OILVALUE1', 'OILVALUE2', 'OPTOILLEVEL',\n",
      "       'POWERDOWNSTATE', 'PULSESIGNS', 'QUSHOILTIMES', 'ROAD', 'ROUNDSPEED',\n",
      "       'TERMINALEL_INSIDE', 'TERMINALEL_OUTSIDE', 'TERMINALSTATUS',\n",
      "       'VERSIONNUM', 'WATERTEMPERATURE', 'WIRELESSTEMP1', 'WIRELESSTEMP2',\n",
      "       'WIRELESSTEMP3', 'WIRELESSTEMP4', 'WORKHOURS'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('共有 %i 个特征列缺失，' % len(all_missing_columns))\n",
    "print('这些特征列为: ',all_missing_columns )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们先将这些全部缺失的特征列全部删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(all_missing_columns,axis=1, inplace=True)\n",
    "test.drop(all_missing_columns,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将剩余存在缺失情况的数据进行可视化分析，我们发现大部分存在缺失的数据所在行数是一样的，即：\n",
    "- 很多样本是同时缺失了多个特征，而不是随机缺失的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ACCRUNTIMES                   1633687\n",
       "ACCSTATE                       241401\n",
       "ACCWORKTIMES                   241401\n",
       "ALARM_0001                     241401\n",
       "ALARM_0002                     241401\n",
       "ALARM_0003                     241401\n",
       "ALARM_0004                     241401\n",
       "ALARM_0005                     241401\n",
       "ALARM_0006                     241401\n",
       "ALARM_0007                     241401\n",
       "ALARM_0008                     241401\n",
       "ALARM_0009                     241401\n",
       "ALARM_0010                     241401\n",
       "ALTITUDE                       241401\n",
       "AREA                             1036\n",
       "CD_VP_UNIQUENO                 241401\n",
       "CITY                             1036\n",
       "CMDTYPE                        241401\n",
       "COLLECTTIME                         0\n",
       "COOLANTLEVEL                   604698\n",
       "COOLANTTEMPERATURE             605322\n",
       "CURRENTFAULTCOUNT1            1553673\n",
       "CURRENTFAULTCOUNT2            1553673\n",
       "CURRENTFAULTCOUNT3            1873600\n",
       "CURRENTFAULTCOUNT4            1873600\n",
       "CURRENTFAULTCOUNT5            1875081\n",
       "CURRENTFAULTCOUNT6            1875081\n",
       "CURRENTSPEEDLOADPERCENTAGE     605647\n",
       "DIRECTION                      241401\n",
       "ELCANDVOL                      241401\n",
       "                               ...   \n",
       "ROTATIONALSPEED                603562\n",
       "SATELLITE                      241405\n",
       "SIMNO                          241401\n",
       "SPEED                          241401\n",
       "SPN                           1633687\n",
       "SPN1                          1553673\n",
       "SPN2                          1553673\n",
       "SPN3                          1873600\n",
       "SPN4                          1873600\n",
       "SPN5                          1875081\n",
       "SPN6                          1875081\n",
       "SPNCONVERSIONMODE1            1660206\n",
       "SPNCONVERSIONMODE2            1873375\n",
       "SPNCONVERSIONMODE3            1873641\n",
       "SPNCONVERSIONMODE4            1875053\n",
       "SPNCONVERSIONMODE5            1875083\n",
       "SPNCONVERSIONMODE6            1875086\n",
       "SPNMSB1                       1553673\n",
       "SPNMSB2                       1553673\n",
       "SPNMSB3                       1873600\n",
       "SPNMSB4                       1873600\n",
       "SPNMSB5                       1875081\n",
       "SPNMSB6                       1875081\n",
       "SYSTEMDATE                     241401\n",
       "TEMPERATURE                   1415749\n",
       "THROTTLECURRENT                605647\n",
       "UNIQUENO                            0\n",
       "VEHICLENO                      241401\n",
       "VEHICLESTATUS                  241401\n",
       "WORKDETAILID                   241401\n",
       "Length: 92, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faac0f68da0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab89065e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(train[['ACCSTATE','ACCWORKTIMES','ALARM_0001','ALARM_0002','ALARM_0005','ALARM_0006','ALARM_0007','ALARM_0008','ALARM_0009','ALARM_0010','AREA']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单值分析\n",
    "除了缺失值，还有一种特征对于模型无法带来增益，就是<font color=\"red\"> 所有特征列都是一样</font>的情况，这种数据往往只能给模型带来噪音，所以在所有的数据中，我们选择将其删除。\n",
    "- 共有8列特征仅仅只包含一个元素值，这些特征列的名称为：\n",
    "> 'ALARM_0001', 'ALARM_0006', 'CMDTYPE', 'COOLANTLEVEL', 'LOCK_ALL', 'LOWVOLTAGESTATUS', 'SPNCONVERSIONMODE6', 'SPNMSB5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "onlyone_val_columns = train.columns[train.nunique(axis=0) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共有 8 个特征列仅仅只包含一个元素之，\n",
      "这些特征列为:  Index(['ALARM_0001', 'ALARM_0006', 'CMDTYPE', 'COOLANTLEVEL', 'LOCK_ALL',\n",
      "       'LOWVOLTAGESTATUS', 'SPNCONVERSIONMODE6', 'SPNMSB5'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('共有 %i 个特征列仅仅只包含一个元素之，' % len(onlyone_val_columns))\n",
    "print('这些特征列为: ',onlyone_val_columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(onlyone_val_columns,axis=1,inplace =True)\n",
    "test.drop(onlyone_val_columns,axis=1,inplace =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label转换 & 分析\n",
    "\n",
    "上面我们删除了很多对于模型训练无用的特征，此处我们对剩余的特征进行重点相关性的分析。首先我们先将我们的类别标签进行拼接。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SPN_FMI_2_Code(Spn_fmis,Codes):\n",
    "    spn_fmi_dict = {}\n",
    "    for i in range(len(Spn_fmis)):\n",
    "        spn_fmi_dict[Spn_fmis[i]] = Codes[i]\n",
    "    return spn_fmi_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbd_faultcode['SPN_FMI'] = dbd_faultcode['SPN'].fillna(-1).astype(int).astype(str) + '_' + dbd_faultcode['FMI'].fillna(-1).astype(int).astype(str)\n",
    "train['SPN_FMI'] = train['SPN'].fillna(-1).astype(int).astype(str) + '_' + train['FMI'].fillna(-1).astype(int).astype(str)\n",
    "\n",
    "spn_fmi_dict = SPN_FMI_2_Code(dbd_faultcode['SPN_FMI'].values,dbd_faultcode['CODE'].values)\n",
    "spn_fmi_dict['-1_-1'] = -1\n",
    "\n",
    "def transfer_2_code(x):\n",
    "    if x in spn_fmi_dict.keys():\n",
    "        return spn_fmi_dict[x]\n",
    "    return -1\n",
    "\n",
    "train['SPN_FMI_Code']  = train['SPN_FMI'].apply(transfer_2_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "code_distribution = pd.DataFrame(train['SPN_FMI_Code'].value_counts().reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Code_Num</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>13</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "    Code  Code_Num\n",
       "0     -1   1649858\n",
       "1   1206    215738\n",
       "2   1239      8194\n",
       "3   1141      1075\n",
       "4   1182       144\n",
       "5   1168        41\n",
       "6   1211        24\n",
       "7   1216         4\n",
       "8   1209         4\n",
       "9   1145         2\n",
       "10  1341         1\n",
       "11  1246         1\n",
       "12  1215         1\n",
       "13  1122         1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code_distribution.columns = ['Code', 'Code_Num']\n",
    "code_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9f4d64b38>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac0f33e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x ='Code', y = 'Code_Num', data = code_distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便后续的预测,此处我们暂时先选取与我们需要预测相关的标签进行分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target = train[train.SPN_FMI_Code.isin([1206,1239,1141,1174,1182])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CODE</th>\n",
       "      <th>SPN</th>\n",
       "      <th>FMI</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>ENGINETYPEID</th>\n",
       "      <th>FAULTSOURCE</th>\n",
       "      <th>FAULTNAME</th>\n",
       "      <th>SPN_FMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1182</td>\n",
       "      <td>4641</td>\n",
       "      <td>2</td>\n",
       "      <td>实际共轨压力低于目标值的控制下限</td>\n",
       "      <td>1958</td>\n",
       "      <td>上柴国三</td>\n",
       "      <td>实际共轨压力低于目标值的控制下限</td>\n",
       "      <td>4641_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1206</td>\n",
       "      <td>8809</td>\n",
       "      <td>15</td>\n",
       "      <td>油水分离器中液位过高</td>\n",
       "      <td>1958</td>\n",
       "      <td>上柴国三</td>\n",
       "      <td>油水分离器中液位过高</td>\n",
       "      <td>8809_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1239</td>\n",
       "      <td>108</td>\n",
       "      <td>4</td>\n",
       "      <td>大气压力传感器电压低于下限门槛值</td>\n",
       "      <td>1958</td>\n",
       "      <td>潍柴国三</td>\n",
       "      <td>大气压力传感器电压低于下限门槛值</td>\n",
       "      <td>108_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1141</td>\n",
       "      <td>8480</td>\n",
       "      <td>2</td>\n",
       "      <td>油门踏板传感器#1和#2信号无效</td>\n",
       "      <td>1958</td>\n",
       "      <td>上柴国三</td>\n",
       "      <td>油门踏板传感器#1和#2信号无效</td>\n",
       "      <td>8480_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1174</td>\n",
       "      <td>9779</td>\n",
       "      <td>5</td>\n",
       "      <td>油泵电磁阀#2开路或对地短路</td>\n",
       "      <td>1958</td>\n",
       "      <td>上柴国三</td>\n",
       "      <td>油泵电磁阀#2开路或对地短路</td>\n",
       "      <td>9779_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CODE   SPN  FMI       DESCRIPTION  ENGINETYPEID FAULTSOURCE  \\\n",
       "7    1182  4641    2  实际共轨压力低于目标值的控制下限          1958        上柴国三   \n",
       "31   1206  8809   15        油水分离器中液位过高          1958        上柴国三   \n",
       "63   1239   108    4  大气压力传感器电压低于下限门槛值          1958        潍柴国三   \n",
       "220  1141  8480    2  油门踏板传感器#1和#2信号无效          1958        上柴国三   \n",
       "251  1174  9779    5    油泵电磁阀#2开路或对地短路          1958        上柴国三   \n",
       "\n",
       "            FAULTNAME  SPN_FMI  \n",
       "7    实际共轨压力低于目标值的控制下限   4641_2  \n",
       "31         油水分离器中液位过高  8809_15  \n",
       "63   大气压力传感器电压低于下限门槛值    108_4  \n",
       "220  油门踏板传感器#1和#2信号无效   8480_2  \n",
       "251    油泵电磁阀#2开路或对地短路   9779_5  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_code = [1206,1239,1141,1174,1182] \n",
    "dbd_faultcode[dbd_faultcode.CODE.isin(target_code)] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据再次清洗\n",
    "因为刚刚对label进行了清洗，为了分析数据特征与标签之间的关系，我们按照之前的两步对数据再一次进行简单的清洗。数据清洗完毕之后，我们只剩下了15列特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "all_missing_columns_step2 = train_target.columns[train_target.isnull().sum() == train_target.shape[0]]\n",
    "onlyone_val_columns_step2  = train_target.columns[train_target.nunique(axis=0) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target.drop(all_missing_columns_step2,axis=1,inplace=True)\n",
    "train_target.drop(onlyone_val_columns_step2,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225151, 15)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACCRUNTIMES</th>\n",
       "      <th>AREA</th>\n",
       "      <th>CITY</th>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th>FAULTTIME</th>\n",
       "      <th>FMI</th>\n",
       "      <th>FRID</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LNG</th>\n",
       "      <th>PROVINCE</th>\n",
       "      <th>READFLAG</th>\n",
       "      <th>SPN</th>\n",
       "      <th>UNIQUENO</th>\n",
       "      <th>SPN_FMI</th>\n",
       "      <th>SPN_FMI_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23150</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:23:45</td>\n",
       "      <td>2018-04-01 06:23:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357817.0</td>\n",
       "      <td>34.108867</td>\n",
       "      <td>34.108867</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23178</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:25:45</td>\n",
       "      <td>2018-04-01 06:25:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357818.0</td>\n",
       "      <td>34.108877</td>\n",
       "      <td>34.108877</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23208</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:27:45</td>\n",
       "      <td>2018-04-01 06:27:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357819.0</td>\n",
       "      <td>34.108891</td>\n",
       "      <td>34.108891</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23240</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:29:45</td>\n",
       "      <td>2018-04-01 06:29:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357821.0</td>\n",
       "      <td>34.109057</td>\n",
       "      <td>34.109057</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23274</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:31:45</td>\n",
       "      <td>2018-04-01 06:31:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357822.0</td>\n",
       "      <td>34.109043</td>\n",
       "      <td>34.109043</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ACCRUNTIMES      AREA      CITY          COLLECTTIME  \\\n",
       "23150        979.0  341322.0  341300.0  2018-04-01 06:23:45   \n",
       "23178        979.0  341322.0  341300.0  2018-04-01 06:25:45   \n",
       "23208        979.0  341322.0  341300.0  2018-04-01 06:27:45   \n",
       "23240        979.0  341322.0  341300.0  2018-04-01 06:29:45   \n",
       "23274        979.0  341322.0  341300.0  2018-04-01 06:31:45   \n",
       "\n",
       "                 FAULTTIME   FMI       FRID        LAT        LNG  PROVINCE  \\\n",
       "23150  2018-04-01 06:23:45  15.0  2357817.0  34.108867  34.108867  340000.0   \n",
       "23178  2018-04-01 06:25:45  15.0  2357818.0  34.108877  34.108877  340000.0   \n",
       "23208  2018-04-01 06:27:45  15.0  2357819.0  34.108891  34.108891  340000.0   \n",
       "23240  2018-04-01 06:29:45  15.0  2357821.0  34.109057  34.109057  340000.0   \n",
       "23274  2018-04-01 06:31:45  15.0  2357822.0  34.109043  34.109043  340000.0   \n",
       "\n",
       "       READFLAG     SPN            UNIQUENO  SPN_FMI  SPN_FMI_Code  \n",
       "23150       1.0  8809.0  201704140835245913  8809_15          1206  \n",
       "23178       1.0  8809.0  201704140835245913  8809_15          1206  \n",
       "23208       1.0  8809.0  201704140835245913  8809_15          1206  \n",
       "23240       1.0  8809.0  201704140835245913  8809_15          1206  \n",
       "23274       1.0  8809.0  201704140835245913  8809_15          1206  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单元变量分析\n",
    "#### ACCRUNTIMES（油门当前值(%)）\n",
    "我们发现：\n",
    "- <font color=\"red\">Code为1239的油门当前值最小为4021%，比其他三个的都要高很多，可以直接通过该值来判别1239这个特征值；</font>\n",
    "- Code为1206的ACCRUNTIMES的分布较为广泛,和其他特征有较多重叠,1182的特征的ACCRUNTIMES值相对最低,低于800."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9f7908e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"SPN_FMI_Code\", y=\"ACCRUNTIMES\", data=train_target);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    71559.000000\n",
       "mean      1054.037354\n",
       "std        926.316787\n",
       "min          0.000000\n",
       "25%        488.000000\n",
       "50%        897.000000\n",
       "75%       1211.000000\n",
       "max       4498.000000\n",
       "Name: ACCRUNTIMES, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['ACCRUNTIMES'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    215738.000000\n",
       "mean        878.156690\n",
       "std         379.871741\n",
       "min           0.000000\n",
       "25%         652.000000\n",
       "50%         877.000000\n",
       "75%        1139.000000\n",
       "max        3527.000000\n",
       "Name: ACCRUNTIMES, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target[train_target.SPN_FMI_Code == 1206]['ACCRUNTIMES'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8194.000000\n",
       "mean     4199.182695\n",
       "std        93.519804\n",
       "min      4021.000000\n",
       "25%      4119.000000\n",
       "50%      4191.000000\n",
       "75%      4281.000000\n",
       "max      4355.000000\n",
       "Name: ACCRUNTIMES, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target[train_target.SPN_FMI_Code == 1239]['ACCRUNTIMES'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1075.000000\n",
       "mean      572.342326\n",
       "std       413.494064\n",
       "min         0.000000\n",
       "25%       167.000000\n",
       "50%       718.000000\n",
       "75%       973.000000\n",
       "max      1627.000000\n",
       "Name: ACCRUNTIMES, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target[train_target.SPN_FMI_Code == 1141]['ACCRUNTIMES'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    144.000000\n",
       "mean     550.500000\n",
       "std      185.069256\n",
       "min        6.000000\n",
       "25%      419.000000\n",
       "50%      620.000000\n",
       "75%      621.000000\n",
       "max      771.000000\n",
       "Name: ACCRUNTIMES, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target[train_target.SPN_FMI_Code == 1182]['ACCRUNTIMES'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.,   7.,  18.,  22.,  39., 418., 419., 620., 621., 770., 771.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(train_target[train_target.SPN_FMI_Code == 1182]['ACCRUNTIMES'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### READFLAG\n",
    "通过观察标签与READFLAG的信息，我们发现：\n",
    "- READFLAG<font color=\"red\">为0的时候只有1206</font>出现了;\n",
    "- READFLAG为1的时候出现了其他所有的标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>READFLAG</th>\n",
       "      <th>SPN_FMI_Code</th>\n",
       "      <th>show_times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1206</td>\n",
       "      <td>4581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1206</td>\n",
       "      <td>211157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1239</td>\n",
       "      <td>8194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1141</td>\n",
       "      <td>1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1182</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   READFLAG  SPN_FMI_Code  show_times\n",
       "0       0.0          1206        4581\n",
       "1       1.0          1206      211157\n",
       "2       1.0          1239        8194\n",
       "3       1.0          1141        1075\n",
       "4       1.0          1182         144"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readflag = pd.DataFrame(train_target.groupby('READFLAG')['SPN_FMI_Code'].value_counts())\n",
    "readflag.columns = ['show_times']\n",
    "readflag.reset_index(inplace=True)\n",
    "readflag.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab5e699320>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab5e68e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x = 'SPN_FMI_Code', y='show_times',hue ='READFLAG',data =readflag, size =10,jitter =True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "features = ['AREA','CITY','COLLECTTIME','FAULTTIME','FRID','LAT','LNG','PROVINCE','UNIQUENO','SPN_FMI_Code']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FRID（自动编号）\n",
    "我们假设<font color=\"red\">相邻编号机器是来自于同一条生产线，出现类似的故障。</font>\n",
    "\n",
    "此处我们取FRID的前五位标志是否属于同一个生产线，他们之间出现相同问题的概率会较大。\n",
    "\n",
    "- 我们随机选取故障编号1141作为分析,发现故障1141的FRID_Top5的分布较为广泛，虽然局部存在聚类的情况，例如24116，24117.24118这三个连续的地方出现了非常多的1141故障。\n",
    "\n",
    "该特征存在一定的聚类情况，后期可以用来平滑预测结果（施加规则修正预测结果）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target['FRID_Top5'] = train_target['FRID'].apply(lambda x: int(str(x)[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab5e6c97f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab5e6fe240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x = 'FRID_Top5', y='SPN_FMI_Code',data =train_target.sort_values('FRID_Top5'), size =10,jitter =True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24117    59\n",
       "24118    39\n",
       "24116    37\n",
       "23952    21\n",
       "25696    12\n",
       "24119    10\n",
       "25314     9\n",
       "25682     8\n",
       "25712     8\n",
       "23797     8\n",
       "24151     7\n",
       "24862     7\n",
       "24909     7\n",
       "24961     7\n",
       "25949     7\n",
       "26219     7\n",
       "26033     6\n",
       "24152     6\n",
       "24068     6\n",
       "24120     6\n",
       "26220     6\n",
       "25429     6\n",
       "23743     6\n",
       "25315     6\n",
       "24908     6\n",
       "24121     5\n",
       "24067     5\n",
       "24159     5\n",
       "25057     5\n",
       "25058     5\n",
       "         ..\n",
       "26200     1\n",
       "26017     1\n",
       "25204     1\n",
       "23971     1\n",
       "25200     1\n",
       "25198     1\n",
       "25194     1\n",
       "23922     1\n",
       "25939     1\n",
       "23782     1\n",
       "25420     1\n",
       "23785     1\n",
       "23792     1\n",
       "23798     1\n",
       "25851     1\n",
       "23806     1\n",
       "25436     1\n",
       "25432     1\n",
       "23810     1\n",
       "25391     1\n",
       "23887     1\n",
       "25845     1\n",
       "25366     1\n",
       "25345     1\n",
       "25341     1\n",
       "23839     1\n",
       "25322     1\n",
       "23850     1\n",
       "25905     1\n",
       "26199     1\n",
       "Name: FRID_Top5, Length: 394, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target[train_target.SPN_FMI_Code == 1141]['FRID_Top5'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24117 1141    59\n",
      "1206    27\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "24118 1206    47\n",
      "1141    39\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "24116 1206    45\n",
      "1141    37\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "23952 1141    21\n",
      "1206     8\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "25696 1206    68\n",
      "1141    12\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "24119 1206    69\n",
      "1141    10\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "25314 1206    62\n",
      "1141     9\n",
      "1239     9\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "25682 1206    62\n",
      "1141     8\n",
      "1239     6\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "25712 1206    69\n",
      "1141     8\n",
      "1239     1\n",
      "Name: SPN_FMI_Code, dtype: int64\n",
      "23797 1206    55\n",
      "1141     8\n",
      "Name: SPN_FMI_Code, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cnt = 10\n",
    "for ind in train_target[train_target.SPN_FMI_Code == 1141]['FRID_Top5'].value_counts().index:\n",
    "    print(ind, train_target[train_target.FRID_Top5 == ind]['SPN_FMI_Code'].value_counts())\n",
    "    cnt -= 1\n",
    "    if cnt==0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PROVINCE\n",
    "\n",
    "假设我们在类似的时间段内往相同的城市输送的机械设备拥有类似的属性，即发生相同问题的概率会很大。而且我们发现训练集中出现的所有城市在测试集中也全部都出现了。我们发现下面的规律：\n",
    "- 设备故障代号为1239的只出现在了PROVINCE代码为15000的城市当中<font color=\"red\">（加上之前我们观察到1239的油门当前值的规律，我们可以直接将城市编号为15000同时油门当前值大于4000的预测为1239）</font>\n",
    "- 除去1239之外，其他的故障代号的机器分布较为广泛，但是还是有大量的城市仅仅只有代号为1206的。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(train_target['PROVINCE'].value_counts().index) - set(test['PROVINCE'].unique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "province_code_distribution = pd.DataFrame({'PROVINCE':train_target['PROVINCE'].unique()})\n",
    "codes = [1206,1141,1182,1239 ]\n",
    "for code in codes:\n",
    "    province_code_distribution[code] = 0\n",
    "    province = train_target[train_target.SPN_FMI_Code == code]['PROVINCE'].unique()\n",
    "    province_code_distribution.loc[province_code_distribution.PROVINCE.isin(province), code] = 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "province_code_distribution.set_index('PROVINCE',inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa33ac2fd0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MVEREwoWcLXMQcBLwmJktSud9ExgB4O5XAscBp5vZWuAd4Hh39wLyFRGRAHWLu7s/AFidmMuAy/JKSkREukZXqIqIlJCKu4hICam4i4iUkIq7iEgJqbiLiJSQiruISAmpuIuIlJCKu4hICam4i4iUkIq7iEgJqbiLiJSQiruISAmpuIuIlJCKu4hICam4i4iUkIq7iEgJqbiLiJRQyD1Uh5vZ782szcyWmtlZVWLMzC41s2VmttjM9ismXRERCRFyD9W1wL+6+0Iz2wx42Mx+6+6PV8SMB3ZJpzHAT9KfIiLSBHU/ubv78+6+MH38BtAGbJcJOwb4qSceBLY0s2G5ZysiIkFCPrmvY2atwL7AvMxL2wErKp63p/Oez/z+ZGAywIgRIz54YcoWG65symvVkygg9rFnnq3++1XsOWPPqvMfO/mxLi03Sug2qBZXVGytPihA1HYtql0RsYXsBxHtqrbPVttfc8lhY+6HRWny/p2X4C9UzWwg8Avga+7+evblKr/iG8xwv8rdR7v76KFDh8ZlKiIiwYKKu5n1JinsP3P326qEtAPDK563AH/penoiItKIkLNlDLgWaHP379cImwVMSM+aORB4zd2frxErIiIFCznmfhBwEvCYmS1K530TGAHg7lcCdwJHAsuAt4FT8k9VRERC1S3u7v4A1Y+pV8Y48JW8khIRka7RFaoiIiWk4i4iUkIq7iIiJaTiLiJSQiruIiIlpOIuIlJCKu4iIiWk4i4iUkIq7iIiJaTiLiJSQiruIiIlpOIuIlJCKu4iIiWk4i4iUkIq7iIiJaTiLiJSQiG32ZtuZi+a2ZIarx9mZq+Z2aJ0ujD/NEVEJEbIbfauBy4DftpJzP3u/slcMhIRkS6r+8nd3ecCL2+EXEREJCd5HXP/iJk9amZ3mdnuOS1TREQaFHJYpp6FwPbu/qaZHQncDuxSLdDMJgOTAUaMGJHDqkVEpJouf3J399fd/c308Z1AbzMbUiP2Kncf7e6jhw4d2tVVi4hIDV0u7ma2rZlZ+viAdJmrurpcERFpXN3DMmZ2I3AYMMTM2oGLgN4A7n4lcBxwupmtBd4Bjnd3LyxjERGpq25xd/cT6rx+GcmpkiIi0k3oClURkRJScRcRKSEVdxGRElJxFxEpIRV3EZESUnEXESkhFXcRkRJScRcRKSEVdxGRElJxFxEpIRV3EZESUnEXESkhFXcRkRJScRcRKSEVdxGRElJxFxEpIRV3EZESqlvczWy6mb1oZktqvG5mdqmZLTOzxWa2X/5piohIjJBP7tcD4zp5fTywSzpNBn7S9bRERKQr6hZ3d58LvNxJyDHATz3xILClmQ3LK0EREYmXxzH37YAVFc/b03kiItIkvXJYhlWZ51UDzSaTHLphxIgR6+a3rv75BrHLa6ysiNhqcbVi32i7pMbaGl8/QOv5v94w9pKjurTcmHZ1NbZaHES2KzC2W7SriP2wSvuh69sgap/dlPbDAvatWjmErr/WcrsaWyvXzuTxyb0dGF7xvAX4S7VAd7/K3Ue7++ihQ4fmsGoREakmj+I+C5iQnjVzIPCauz+fw3JFRKRBdQ/LmNmNwGHAEDNrBy4CegO4+5XAncCRwDLgbeCUopIVEZEwdYu7u59Q53UHvpJbRiIi0mW6QlVEpIRU3EVESkjFXUSkhFTcRURKSMVdRKSEVNxFREpIxV1EpIRU3EVESkjFXUSkhFTcRURKSMVdRKSEVNxFREpIxV1EpIRU3EVESkjFXUSkhFTcRURKSMVdRKSEgoq7mY0zsyfNbJmZnV/l9YlmttLMFqXTqfmnKiIioULuodoTuBwYC7QDD5nZLHd/PBN6k7ufUUCOIiISKeST+wHAMnd/2t3fBWYCxxSbloiIdEVIcd8OWFHxvD2dl3WsmS02s1vNbHgu2YmISENCirtVmeeZ53cAre6+F/A7YEbVBZlNNrMFZrZg5cqVcZmKiEiwkOLeDlR+Em8B/lIZ4O6r3H1N+vRqYP9qC3L3q9x9tLuPHjp0aCP5iohIgJDi/hCwi5ntYGZ9gOOBWZUBZjas4unRQFt+KYqISKy6Z8u4+1ozOwO4G+gJTHf3pWb2HWCBu88CvmpmRwNrgZeBiQXmLCIiddQt7gDufidwZ2behRWPLwAuyDc1ERFplK5QFREpIRV3EZESUnEXESkhFXcRkRJScRcRKSEVdxGRElJxFxEpIRV3EZESUnEXESkhFXcRkRJScRcRKSEVdxGRElJxFxEpIRV3EZESUnEXESkhFXcRkRJScRcRKaGg4m5m48zsSTNbZmbnV3m9r5ndlL4+z8xa805URETC1S3uZtYTuBwYD4wCTjCzUZmwScAr7r4z8ANgWt6JiohIuJBP7gcAy9z9aXd/F5gJHJOJOQaYkT6+Ffi4mVl+aYqISIyQ4r4dsKLieXs6r2qMu68FXgMG55GgiIjEM3fvPMDss8An3P3U9PlJwAHufmZFzNI0pj19/qc0ZlVmWZOByenTfwCerLLKIcBLAbmHxm1qsc1ef1GxzV5/UbHNXn9Rsc1ef1GxzV5/HrHbu/vQur/p7p1OwEeAuyueXwBckIm5G/hI+rhXmozVW3aN9S3IM25Ti232+tUutas7rF/tio/NTiGHZR4CdjGzHcysD3A8MCsTMws4OX18HHCvp5mJiMjG16tegLuvNbMzSD6d9wSmu/tSM/sOybvKLOBa4AYzWwa8TPIGICIiTVK3uAO4+53AnZl5F1Y8Xg18Nqecrso5blOLbfb6i4pt9vqLim32+ouKbfb6i4pt9vqLjF1P3S9URURk06PhB0RESkjFXUSkhFTcRURKKOgL1aKkQxQcQHKFqwN/AeZXO43SzLYAxmVi73b3VzdivkE5mNluJEMyVMbNcve2RpdZVGxkHzS9vyK3bXBsxPpj+iBo/UVt1yJy7S45pPGDAHf3V6q9XlS7YvftZtWupn1yN7MjgKeAKcCRwFHAt4Gn0tcqYycAC4HDgP7AAOB/AQ+nr2WXvZuZnWdml5rZj9LHI2vkERQbmoOZnUcy/o4B80muEzDgxuyImjHtKiI2sg+K7K8tzOzzZvZ1Mzs7fbxllbiYbRscG5pDZB8Erb+o7VpErt0hBzMbYWYzzWwlMA94yMxeTOe1Ft2uBvbt2PjPmNlTZvaamb1uZm+Y2evZuCCNXv3U1QloA1qrzN8BaMvMexLYskrsVsAfM/POAxYB5wP/nE7nd8zrQmxQDsAfgd5V4voAT3WhXbnHRvZBUf01AfgT8BPgW+l0ZTpvQiY2ZtvGxAblENmuoPUXuF1zz7U75AD8D/B5oGfFvJ4k19Y8WHS7YtrUYPwyYGR2fiNTMw/L9CIZhCzrOaB3Zp6R/DuT9bf0tUqTgN3d/b31FmD2fWApcEmDsaE5/A34MPDnTNyw9LX1VhW4zKJiY/qgqP76N2B/3/Bf361IPpn9NPP7ods2JjY0h5h2ha6/qO1aRK7dIYch7n5TZYC7vw/MNLP/0+AyY3KNaVMj8X/1Lhw2rNTM4j6d5F+qmXww6uRwknfgazOxFwMLzew3FbEjgLFAVzo0JjY0h68B95jZU5m4nYEzutCuImJj+qCo/orZ+WO2bUxsaA4x7Qpdf1HbtYhcu0MOD5vZFSRDjFdur5OBRzZCu2La1Ej8AjO7CbgdWNMx091vqxLbqaZexJQe2+74ssNIPsHMcvfHq8RuBXwiE3u3Z75MMbNxwGUkxzE36FB3n9NIbGQOPfjgC7KOuIfSTxgNtauo2Mg+KKK/TgYuBKru/O5+fSY+ZtsGxcbkENkHoevPfbsWlWuzc7BkfKtJme21ArgDuNbd18QuMzbXmDY1sA2uq7IId/cvVlt2Z0p5hWoRBUCKE/vHUtYcRHKVx4H7vCdgSkTsVd0g36AcgNlFtKuI2Mg+aHp/RW7b4NiC+iBo/UVt1yJy7Q45AJ9sZrti9+1q8UA/4CvAFSSH7KaTDNYYvNyOqbtexPRwROz/Cw00s9lFxEbk8KUClllUbEwfFNVfMYMmxWzb4NiIHGL6IHT9hWzXyNjusM+G5vCPBSwTwnONaVOt+BuAbUn+i/wD0AK8EblcoKSHZWoxs2Hu/nzesZ0so+5FFlKbme3v7lULXMy27Uo/dJZDxDI2mf2gO+RaLwdr4OK07tCuEGb2iLvva2aL3X0vM+tNcnjwY9ELa+Tjfh4TyZk6pwFzgMXAo8BdwL+QOTcV2ILktMQngFXp1JbO2+Ac0orfGwRsFZhPp7GhOZB8ETcTWEnyRe0y4MV0Xmuj7SoiNrIPCu+vgD6K2bbBsRHrj+mDoPUXtV2LyLU75EDctSm5tyumTQ3Gz09/zgX2ILnN3tMN7a+N/qF1dQJuJLlo5ECSfz1a0sc/AW7KxN6dduq2FfO2Tef9dmMUgNAciLvIIqZducdG9kFR/RVTAGK2bUxs6B92TLuC1l/gds091+6QA3EXJuXerpg2NRh/KskFTocAT5PUo9OycSFTM4v7k528tsGVbqHLiezQmNigHLI7WCZug6viItqVe2yOfdCV/oopADHbNiY29A87pl1B6y9wu+aea3fIgeQNePsqMdtvjHbFtKnB+L7AF4BvAhel04W1ltHZ1MwvVF8xs8+mpyICyWmJZvZ5IHtc7M9m9g0z26Yidpt07IgVmdgh7n6TV5zK6O7vu/tMYHAXYkNzeNjMrjCzMWb24XQak154kb3IIqZdRcTG9EFR/dXq7tPc/YWOGe7+grtPI/nPqlLMto2JDc0hpl2h6y9quxaRa3fIoePCpLvM7Kp0mgPcA5y1EdoV06ZG4n9F8n3CWuDNdHqrSlx9jbwj5DEBrcBNJIdE/khyWGRlOm+HTOxWwDSSd+1X0qktnTcoEzuT5DSiMSRXn344fXwFcHMXYoNyIPn38HSSY6iPAUvSx18G+nahXbnHRvZBUf31G+AbwDYV87Yh+dT8u0xszLaNiQ3KIbJdQesvcLvmnms3yqEHyaGrY4Hj0sc9K2OKaldMmxqMX5JXje0WZ8uY2WCSM3deymFZ1a5gawdmkbmCLSa27GL6IOf+2orkC7FjSAoqwAskfTDN3V/u6jo2hRzSPHLbrrJpsuTU2x+7+2NdXlYzi7tVP6XpV+7+RJXYTwCfqhI7JxtbYL51czCzXiRvGBvEkbxhZAcpC25XEbGRfdDU/orZtrH9EJFD6HaNybWQ7VpErt0lhxBFtSt2347cXo+TDH/yDMnYMkZyCude0e1vVnFPjzmdQHJopGNUvBaSLzNnuvslFbE/BHYlGZ2vMnYCyRcjZ1XEFlIAQnMwsxuBV0kGNqqMO5nk37DPN9iu3GMj+6CQ/krjQ/+oYrZtcGxoDpF9ELT+Av8Ocs+1u+QQqoh2NbBvx8ZvX60t7p4d3LC+vI7vxE5Ejs1dYxlWJbao0/uCciDu7IeYduUeG9sHBfXXD4E7SYrZR9Pp+HTejzKxRZ3dE5RDZLuC1l/gds091+6SQ+hURLti2tRIfJ5TM8+W6RhuN6vacLurzeyAKrH/CKzOzNvP3U939wfdvT2dHnT304F9uxAbmkPM2Q8x7SoiNqYPiuqvI939SHef6e4PpNNMkjsSHZmJLersntAcYtoVuv6itmsRuXaXHEIV0a6YNjUSn5tmjuceM9byROAnZrYZH/xrMxx4PX2t0itm9lngF+7+N6Bj5MfPUqMABMaG5nA8yTfhV5jZKyTv0FsC96avNdquImKLGh89JtfVZnaAu8/PzK+288ds25jY0Bxi2hW6/qK2axG5dpccQhXRrpg2NRKfm2Z/oRo13K6ZbVsZ6xXnJVfEtJJ06MdICnRlh57v7s80EhuTQ0Vs0NkPkcvMNTamDwrqr/1IDoNV2/m/7LXHlsnt7J7YHGL6IHD9uW/XonLtTjnEyLtdDbQpKj4PzfzkDskXVx3T3yp+bsCSO4gfSsUXXma2wR3E3X05yVWndTs0JjYmB8uc/WBmnZ39ELTMAmOD+yAmNqK/FgJjIv6oYrZtUGxMDpF9EJpr7tu1wFy7RQ6himhXTJsaic9L0465W0F3fU/jd7PkLISLgH83s/PSTq6WR1BsaA5W+47rM23j3Uk+NNeYPiiyvzp2/kOBg4FDzWzLKnEx2zY4NjT9UCwpAAAOQ0lEQVSHyD4IWn9R27WIXLtLDqGKaFcD+3ZUfK6K/La2s4ni7vp+HuGjxsXEht4dvTvcST4015g+KKq/JgB/Ijks8q10ujKdNyETW9TZPUE5RLYraP0Fbtfcc+0uOYRORbQrpk2NxOc5NfOwTFF3fZ8E7O4bXqDwfWApyUh/jcSG5hBz0+2YdhURG9MHRfXXvwH7+4b/+m4FzCM5P7jy94u4+XloDjHtCl1/Udu1iFy7Sw6himhXTJsaic9NM4v7dIq563tRBSA0h+5wJ/nQ2Jg+KKq/Ynb+os7uCc0hpl2h6y9quxaRa3fJIVQR7YppUyPxuWn22TKjgKPJ8a7vZjYOuIzkOOYGHerrX3EYHBuZQ3e4k3xorjF9UER/nQxcSDJ41wY7v7tfn4nP/eyemBwi+yB0/blv16Jy7S45hCqiXTFtaiQ+L91l4LCgW2BZMmzmum+c3f2vNeKKPL2vbg5mZhXLdJJL2ed7jY0d2q6CY3O/bV1Ef8UUgOBtGxkbk0Nou2L3g1y3a8G5Nj2HEEW1K6b9jcTnoWmHZcxsBPCfJOeYv5bO24IPzjFfXhG7D8kXXFuQ/NEZ0GJmr5Kch7wws/jgU8tCY0NzSM9wuILkv4Hn0l9vAXY2sy+7+28aaVcRsZF9UFh/ufsrZvZ71t/5qxXVmG0bHBuaQ2QfBK2/qO1aRK7dJYdQRbQrdt9uoHblp8hvazubiLsL0iJgTJVlHAg8mpl3BMnt8u4CrkmnOem8I7oQG5QDcWc/xLQr99jIPiiqv/YBHky322+B35GMff0gyfAQlbFFnd0TlENku4LWX+B2zT3X7pJD6FREu2La1Eh8nlNhCw7Y8HndMm1ZFzo0JjYoB5JPCb2qxPSpkmtMu3KPzbEPutJfMQUgattGxIb+YUf1Qcj6C9yuuefaXXIInYpoV0ybGonPc2rm2TIPW3K7qxmsf5bAyWx4C6y7zOzXJKekVcZOIPmkXamo0/tCc4g5+yGmXUXExvRBUf01wN3nZebh7g+a2YDM7KLO7gnNIaZdoesvarsWkWt3ySFUEe2KaVMj8blp5njuUXdBMrPx1WLd/c5M3AXA50iuTMt26M3uPrWR2MgcYs5+CFpmEbExfVBgf10K7ET1nf8Zdz8jE5/72T0xOUT2wcgasY9XxBSyXdPYI2u0v6FcG8wh1+0VK7JdQdsrpk0xy81btzhbJm+RHZr7DiVxYv9YCsqhoT9AM9va3V/cCCmWgrbXRlTkMZ/YidoD2+9V8bg3yeXhs4DvAv03Um5nAEPSxzsBc0lGkpwH7FkRtzkwFbgBOCGzjCuKaBdwVeb5bcCJwMA6v9ef5MbQ5wL9SA4FzCI5e2NgF2K3ILm6tw1YlU5t6bwNLsWuktfWNebHbNvg2IjtPKjKtJzkUvLsDZ/HZbbHNcBi4OdU3Ig78u+gJ3AaycUv/5R57VuZ59uSDKdwOTCYZOyaxcDNwLCINt/VhRyCt1dMDnlMVdoVtL0IrAN590MjUzMPy7xBcspZ5RWA/YG3Sc713bwidqG775c+/h7JRrqO5LZog909aAAeM7vL3cdXPB/n6YVK6eln3yM5J3YJcLZXnItqZkvdfff08a+Ba9z9l2Z2GHCxux+UvvYLki9yHgS+CLwHfMHd11S2I7ZdlpwDXbVZJF/6tVTEPkdyFsbHSM78uBH4tbu/m9keN5MchvgQ8A8kBfhm4H8D27r7SQ3G3k1yKt8MT0dWtGTExYnAx919bJ12LSS5WYp5xc2pI7dtTOy2JAPH/Y3kYqYzgc+QnDFzlrs/n8b9jQ2vZm4h+ZTv7r5jxTIr+/YakhtuX50u91B3/1T6WszfwTXpa/OBk4A/uPvXs+tLn88Bfk0yUNUXgJ+R7AfHAIe7+zEVset+L8OA2e4+rMEcYrZXcA6hItsVtL1C60DscgtR5DtHnXfOH5Mc49ymYt4zNWIfqXi8iHQwIJJOWpyJ3a/GtD/wfCZ2YcXja4D/ALYHzgZuz8Q+WfH4ocxriyvzy7z2b8B/kxTuhV1o1/vA0yQ3zu2YOp6/W225JOOTn0Ryu7iVJG8cR2RzTdf3Ah8cpqu2/pjYzm5v9mTm+d8ybXqGpBA/AzxdLYfAbRsTO4ekoJ9P8qnqPJIrVM8kGR62I+6cNLbyP7Va++zCTnJZVPE45u+gcj/rBVxF8l9a38p9qcq+9WydbfM+yZvx76tM73Qhh5jtFZxD6BTZrqDtRWAdaKQf8p4KW3Dgxt8/3fhfJRl++OkacU8DnwaOZcNTFLOny8V0aNAfYPr8YuB6YEfgmyTjVowATiH5FNAR1wb0yPzuySQDkf25Srs+E9iup4ARNbbPilrtqpg3CPgX4N4aO+30OuuPif0NySGcyoK1DUnR/F0mNqYAxGzbmNiYQtgC3AJ8n+TNs9Y+2w58HfjXtJ+t4rXsm2Ho38ETVeZdRPKmlT1tsvIUzv/IvJZd/xJgl8B9KziHyO0VnEPoFNmuoO1FYB0IXO5jjbQruP1FLjywA3qkO/X9JFcFVou5nuRTZ8e0TTp/W+CeLnRo8B9gOm8iybG1l4A3gMdJjo9vURHznyT/bmV/d1yVP8DrCG/XV4C9a7TrzMzzuYHb/hqqHJcnOZb4QBdityK5w9UTJMcjXyYpttOocqw1ogDEbNuY2Og/QJLDUQ8CL9R4/aLMNLSib3/a4N/Bf1FxLL9i/qnAe5l536nRXzsDt2bmHQf8Q411fqrRHCK3V3AOoVNku2K21ynUqQONLDfvqbAFB278A4B/TB8fTHK888jA393gD6SBDo39A6zMd3eSN4UjMzFjOjqZ5Pj0d4A7SApbtc4fU7HMUSRvNl3aBlVyDV5uxzKpeKOrs8yjqsVmfu/gdFsdUSeu0wKQxuwGfDz7BwOMr7Psj6b5bpBDo3+Aabu+VWOZlftB/3r7QcXvDQNWhfR/vX0gov1jgM0j9tmgfSuz3P4kb7i/q7cN6uUbM3Wyv4zrJNcPkdwwpW5/pfE35JFD3lMzv1C9CBhPctzutyQ7zB+Aw0kGbLq4InZWlUV8jORfWdz96MB1nuLu1zUSG5qvmS0l+YS91syuIvli7FaSzt3b3T+T0zYwkju6bLANqix3DHBflVxzX2YaO9/dD0gfn0ryX8ftJMM93OHulePkr5+A2YeAndx9SZU+OJPkbIU2kiEDznL3X6WvZb/Mq8zhS2kOvwzJIZPPuhyqLPPLtdoVuh/E7NuR/RXc/i7us53tB9nlvgX8osZyc+mv9TaO2VfT5YTsL7n3VxofvM/mrsh3jjrvZo+RnFbVn+RGxJXvmtljgo+Q/Dt4GMlt0A4Dnk8fHxqxzmcbjQ3Nl/XHDan3JV8h2yAi19yX2bHciscP8cF/RAOIOM5Yow8Gpo9bgQUkfyzrrbOoHGKWGbofkJwZFNoHMf2Ve64N7Acxy82lv7qwv+TeX7E55D01c/iBtZ4Mq/u2mf3J3V8HcPd30lOoKu0PnEVyxsO57r7IzN5x9z9kF2pmi2usz0i+1GsoNiLfyk+cj5rZaHdfYGa7kpwFUvg2iFhuEcsE6GHJELo9SA7ZrExj3zKztZWBkX3Q093fTJe1PD397FYz2571TyUsKofgZRK+H4wmvA9i+quIXCFuP4hZbky+oWL2lyL6KzaHfBX5zlHnXXUe6YU6VJzVQHLBxwZne6SvdXzxdhk1PoUDfyX592f7zNRK5ouqyNigfNPn15Pcf3MeyY7xNMnhlr030jaIWm7eyyS5UKXjNM2nSc6DBxjIhp/YYvrgXmCfzLxeJKcSvl90DpHLDN4PQvsgsr8KyTVyP4hZbnC+ETUmZn8ppL9icsh7KmzBARu+b435Q6hypVcm5ijguzVeuxb4aI3Xft6F2Kh8Sc762Jvk01bVKxIL3AYNLbeIZWZi+wM7dKEPWjr+6KvEHrQxcghdZsx+ENoHXYnNK9dG9oPYbRCab8DvRu8vefdXHvtso1Mpx5YREfl716PZCYiISP5U3EVESkjFXTY5Zva+mS0ysyVmdouZ9a8y/w4z27Lid3Y3s3vN7I9m9pSZ/bslWs2s3ZIbpVeuY5GZHWBmU8zsnHTe9Wb2nJn1TZ8PMbPlFb+zq5ndaWbLzKzNzG42s23M7DAzey1dZsd0+EbZWPJ3S8VdNkXvuPs+7r4H8C7JmDnZ+S+TXMDScWHULOASd9+V5AuzfyK5QfFyktEuD+5YuJntBmzm7vOrrPt9klEm12Nm/UhG//uJu+/s7iNJhnodmobcn+bWMf2ui9tApFMq7rKpu59kmICs/yG58QYkQ63+t6d3u3f3t0muGjw/ff1GkrtvdTg+nVfND4GzzSx7jcgXgP9x9zs6Zrj77919SURbRHKj4i6brLTAjie5CrByfk+Sy8Y7LhXfHXi4Msbd/wQMNLPNScal/1RFwf48ya0Xq3kWeIBkKOVKe2TXkXFw5rDMTp02TqSLmnmFqkijPmRmi9LH9/PBzY475reSFNrfpvON5IYY1bi7v5COLfJxM/sryeiGnX3i/i7JG8evI3K+390/GREv0iUq7rIpesfd96k135K7as0mOeZ+KckY7odUBprZjsCb7v5GOqvj0MxfqX1IBgB3X5a+iXyuYvZSkvFFRLoFHZaR0nH310jGRj/HzHqT3Nrsox1nqKRfsF5KMgRth18AR9L5IZlKF5PcaKTDz4F/MrOjOmaY2Tgz27MrbRFplIq7lJK7PwI8Chzv7u+Q3LPyW2b2JMkx+odIxgXpiH+VZCz5v7r7MwHLX0oyQmDH83eATwJnpqdaPk5yc5cX05DsMffj8minSC0afkBEpIT0yV1EpIRU3EVESkjFXUSkhFTcRURKSMVdRKSEVNxFREpIxV1EpIRU3EVESuj/A939iik2kRJJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa33ecae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "province_code_distribution.plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AREA\n",
    "该类可以认为是PROVINCE的子类，分析的思路和PROVINCE类似。\n",
    "\n",
    "我们发现：\n",
    "- 当我们划分到具体的区域的时候，很多Area都只存在一种机器故障。<font color=\"red\">此时我们可以通过ont-hot编码或者编码排序的方式</font>得到更好的判别结果。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 每个区域对应一个省市**， 验证了我们AREA为PROVINCE的子类的猜测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AREA\n",
       "0.0         1\n",
       "450105.0    1\n",
       "441802.0    1\n",
       "441721.0    1\n",
       "441521.0    1\n",
       "441323.0    1\n",
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       "441203.0    1\n",
       "440983.0    1\n",
       "433130.0    1\n",
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       "430503.0    1\n",
       "450109.0    1\n",
       "450123.0    1\n",
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       "451222.0    1\n",
       "451123.0    1\n",
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       "410704.0    1\n",
       "410526.0    1\n",
       "410522.0    1\n",
       "410503.0    1\n",
       "410481.0    1\n",
       "410122.0    1\n",
       "           ..\n",
       "370725.0    1\n",
       "370724.0    1\n",
       "370705.0    1\n",
       "370704.0    1\n",
       "370611.0    1\n",
       "370521.0    1\n",
       "370481.0    1\n",
       "370405.0    1\n",
       "370321.0    1\n",
       "370303.0    1\n",
       "350623.0    1\n",
       "350125.0    1\n",
       "341821.0    1\n",
       "340221.0    1\n",
       "340203.0    1\n",
       "340202.0    1\n",
       "340124.0    1\n",
       "340123.0    1\n",
       "340122.0    1\n",
       "340121.0    1\n",
       "340111.0    1\n",
       "340104.0    1\n",
       "331004.0    1\n",
       "331003.0    1\n",
       "330825.0    1\n",
       "330783.0    1\n",
       "330782.0    1\n",
       "330681.0    1\n",
       "340303.0    1\n",
       "340321.0    1\n",
       "340521.0    1\n",
       "340711.0    1\n",
       "341802.0    1\n",
       "341522.0    1\n",
       "341502.0    1\n",
       "341322.0    1\n",
       "341321.0    1\n",
       "341302.0    1\n",
       "341282.0    1\n",
       "330521.0    1\n",
       "341226.0    1\n",
       "341125.0    1\n",
       "341124.0    1\n",
       "341022.0    1\n",
       "340827.0    1\n",
       "340822.0    1\n",
       "340811.0    1\n",
       "340802.0    1\n",
       "341225.0    1\n",
       "654322.0    1\n",
       "Name: CITY, Length: 307, dtype: int64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.groupby('AREA')['CITY'].nunique().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AREA\n",
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       "Name: PROVINCE, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.groupby('AREA')['PROVINCE'].nunique().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_code_distribution = pd.DataFrame({'AREA':train_target['AREA'].unique()})\n",
    "codes = [1206,1141,1182,1239 ]\n",
    "for code in codes:\n",
    "    area_code_distribution[code] = 0\n",
    "    area = train_target[train_target.SPN_FMI_Code == code]['AREA'].unique()\n",
    "    area_code_distribution.loc[area_code_distribution.AREA.isin(area), code] = 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa2b8b8128>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa2a563eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "area_code_distribution.set_index('AREA',inplace= True)\n",
    "area_code_distribution.plot(kind='bar',stacked=True, figsize=[16,12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### COLLECTTIME & FAULTTIME\n",
    "\n",
    "我们发现：\n",
    "- COLLECTTIME和FAULTTIME是全部一样的，所以我们仅仅只需要选择一个进行分析即可。\n",
    "\n",
    "此处我们认为在相同时间内的预测结果是可信的，而超过训练集时间的我们认为是较难保证预测准确性的。\n",
    "\n",
    "- 我们发现训练集和测试集中的时间不在同一个时间段内，如果<font color=red>单独使用该特征并不能给我们带来非常大的帮助</font>。\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225151"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(train_target['COLLECTTIME'] == train_target['FAULTTIME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225151, 17)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************************************************** \n",
      " Min time in train dataset is: 2018-04-01 06:23:45  Max time in train dataset is: 2018-04-30 23:58:00\n",
      "**************************************************************************************************** \n",
      " Min time in test dataset is: 2018-05-01 00:00:03  Max time in test dataset is: 2018-05-10 23:58:58\n"
     ]
    }
   ],
   "source": [
    "print('*'*100,'\\n', 'Min time in train dataset is:',train_target['COLLECTTIME'].min(), ' Max time in train dataset is:',train_target['COLLECTTIME'].max())\n",
    "print('*'*100,'\\n', 'Min time in test dataset is:',test['COLLECTTIME'].min(), ' Max time in test dataset is:',test['COLLECTTIME'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们假设在相同时间段检测的产品拥有一定的共性，即如果有个机械设备在某一时间出故障，那么可能在此时间周围出故障的类型是相同的。\n",
    "- 我们随机选取8天进行观察，发现基本满足我们的假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target['COLLECTTIME'] = pd.to_datetime(train_target['COLLECTTIME'])\n",
    "train_target['Day']         = train_target['COLLECTTIME'].apply(lambda x:x.day)\n",
    "train_target['Hour']        = train_target['COLLECTTIME'].apply(lambda x:x.hour)\n",
    "train_target['Minute']      = train_target['COLLECTTIME'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACCRUNTIMES</th>\n",
       "      <th>AREA</th>\n",
       "      <th>CITY</th>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th>FAULTTIME</th>\n",
       "      <th>FMI</th>\n",
       "      <th>FRID</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LNG</th>\n",
       "      <th>PROVINCE</th>\n",
       "      <th>READFLAG</th>\n",
       "      <th>SPN</th>\n",
       "      <th>UNIQUENO</th>\n",
       "      <th>SPN_FMI</th>\n",
       "      <th>SPN_FMI_Code</th>\n",
       "      <th>FRID_Top5</th>\n",
       "      <th>UNIQUENO_LAST4</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23150</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:23:45</td>\n",
       "      <td>2018-04-01 06:23:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357817.0</td>\n",
       "      <td>34.108867</td>\n",
       "      <td>34.108867</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23178</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:25:45</td>\n",
       "      <td>2018-04-01 06:25:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357818.0</td>\n",
       "      <td>34.108877</td>\n",
       "      <td>34.108877</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23208</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:27:45</td>\n",
       "      <td>2018-04-01 06:27:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357819.0</td>\n",
       "      <td>34.108891</td>\n",
       "      <td>34.108891</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23240</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:29:45</td>\n",
       "      <td>2018-04-01 06:29:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357821.0</td>\n",
       "      <td>34.109057</td>\n",
       "      <td>34.109057</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23274</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:31:45</td>\n",
       "      <td>2018-04-01 06:31:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357822.0</td>\n",
       "      <td>34.109043</td>\n",
       "      <td>34.109043</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ACCRUNTIMES      AREA      CITY         COLLECTTIME  \\\n",
       "23150        979.0  341322.0  341300.0 2018-04-01 06:23:45   \n",
       "23178        979.0  341322.0  341300.0 2018-04-01 06:25:45   \n",
       "23208        979.0  341322.0  341300.0 2018-04-01 06:27:45   \n",
       "23240        979.0  341322.0  341300.0 2018-04-01 06:29:45   \n",
       "23274        979.0  341322.0  341300.0 2018-04-01 06:31:45   \n",
       "\n",
       "                 FAULTTIME   FMI       FRID        LAT        LNG  PROVINCE  \\\n",
       "23150  2018-04-01 06:23:45  15.0  2357817.0  34.108867  34.108867  340000.0   \n",
       "23178  2018-04-01 06:25:45  15.0  2357818.0  34.108877  34.108877  340000.0   \n",
       "23208  2018-04-01 06:27:45  15.0  2357819.0  34.108891  34.108891  340000.0   \n",
       "23240  2018-04-01 06:29:45  15.0  2357821.0  34.109057  34.109057  340000.0   \n",
       "23274  2018-04-01 06:31:45  15.0  2357822.0  34.109043  34.109043  340000.0   \n",
       "\n",
       "       READFLAG     SPN            UNIQUENO  SPN_FMI  SPN_FMI_Code  FRID_Top5  \\\n",
       "23150       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23178       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23208       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23240       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23274       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "\n",
       "       UNIQUENO_LAST4  Day  Hour  Minute  \n",
       "23150             591    1     6      23  \n",
       "23178             591    1     6      25  \n",
       "23208             591    1     6      27  \n",
       "23240             591    1     6      29  \n",
       "23274             591    1     6      31  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa216f5780>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa22379e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=2,figsize = [12,6]) \n",
    "train_target_10 = train_target.loc[train_target.Day == 10] \n",
    "train_target_11 = train_target.loc[train_target.Day == 11] \n",
    "train_target_12 = train_target.loc[train_target.Day == 12] \n",
    "train_target_13 = train_target.loc[train_target.Day == 13] \n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_10.sort_values('Hour'), size =10,jitter =True,ax=ax[0,0])\n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_11.sort_values('Hour'), size =10,jitter =True,ax=ax[0,1])\n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_12.sort_values('Hour'), size =10,jitter =True,ax=ax[1,0])\n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_13.sort_values('Hour'), size =10,jitter =True,ax=ax[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa20aeed30>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa20be3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=2,figsize = [12,6]) \n",
    "train_target_14 = train_target.loc[train_target.Day == 14] \n",
    "train_target_15 = train_target.loc[train_target.Day == 15] \n",
    "train_target_16 = train_target.loc[train_target.Day == 16] \n",
    "train_target_17 = train_target.loc[train_target.Day == 17] \n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_14.sort_values('Hour'), size =10,jitter =True,ax=ax[0,0])\n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_15.sort_values('Hour'), size =10,jitter =True,ax=ax[0,1])\n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_16.sort_values('Hour'), size =10,jitter =True,ax=ax[1,0])\n",
    "sns.stripplot(x = 'Hour', y='SPN_FMI_Code',data =train_target_17.sort_values('Hour'), size =10,jitter =True,ax=ax[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### UNIQUENO\n",
    "\n",
    "上面我们对采集时间进行了分析，下面我们对另外一个特征列UNIQUENO进行分析。我们可以得到如下的结论：\n",
    "\n",
    "- UNIQUENO拥有一定的结构性，前面是日期属性,后面是产品代号，例如：\n",
    "> 201701241116166406, 前面的20170124111616就是时间属性2017:01:24,11:16:16,可能是产品生产出来的时间，最后几位是产品的代号。\n",
    "\n",
    "- 我们发现该类特征具有较强的聚类属性， <font color=\"red\">同一个UNIQUENO往往只对应一种错误的故障代码（仅有少量的两个对应了两个故障代码）</font>。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201704241116166406    8194\n",
       "201705201829567748    7732\n",
       "201705241030007990    7012\n",
       "201705101530077175    4938\n",
       "201704180930016169    4575\n",
       "201701201030011850    4316\n",
       "Name: UNIQUENO, dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target['UNIQUENO'].value_counts().head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现该类特征具有较强的聚类属性， 同一个UNIQUENO往往只对应一种错误的故障代码（仅有少量的两个对应了两个故障代码）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNIQUENO\n",
       "201704131442275879    1\n",
       "201704150830046013    1\n",
       "201704150830046014    1\n",
       "201705151007377433    2\n",
       "201701231030031929    2\n",
       "201703301130045382    2\n",
       "201704111435245794    2\n",
       "201804081727258207    2\n",
       "201704111435245782    2\n",
       "201702211030013081    2\n",
       "Name: SPN_FMI_Code, dtype: int64"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.groupby('UNIQUENO')['SPN_FMI_Code'].nunique().sort_values().tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa1cdc8f28>"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa1cfebbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=3,figsize = [12,8]) \n",
    "train_target_UNIQUENO_1 = train_target.loc[train_target.UNIQUENO == 201704241116166406]['SPN_FMI_Code'].value_counts()\n",
    "train_target_UNIQUENO_2 = train_target.loc[train_target.UNIQUENO == 201705201829567748]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_3 = train_target.loc[train_target.UNIQUENO == 201705241030007990]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_4 = train_target.loc[train_target.UNIQUENO == 201705101530077175]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_5 = train_target.loc[train_target.UNIQUENO == 201704180930016169]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_6 = train_target.loc[train_target.UNIQUENO == 201701201030011850]['SPN_FMI_Code'].value_counts()  \n",
    "\n",
    "train_target_UNIQUENO_1.plot(kind='bar',stacked=False,ax=ax[0,0])\n",
    "train_target_UNIQUENO_2.plot(kind='bar',stacked=False,ax=ax[0,1])\n",
    "train_target_UNIQUENO_3.plot(kind='bar',stacked=False,ax=ax[0,2])\n",
    "train_target_UNIQUENO_4.plot(kind='bar',stacked=False,ax=ax[1,0])\n",
    "train_target_UNIQUENO_5.plot(kind='bar',stacked=False,ax=ax[1,1])\n",
    "train_target_UNIQUENO_6.plot(kind='bar',stacked=False,ax=ax[1,2])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对应两种编码的时候基本其中一种也占据了较大的优势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa1cfda2b0>"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa1d323d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=3,figsize = [12,8]) \n",
    "train_target_UNIQUENO_1 = train_target.loc[train_target.UNIQUENO == 201705151007377433]['SPN_FMI_Code'].value_counts()\n",
    "train_target_UNIQUENO_2 = train_target.loc[train_target.UNIQUENO == 201701231030031929]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_3 = train_target.loc[train_target.UNIQUENO == 201703301130045382]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_4 = train_target.loc[train_target.UNIQUENO == 201704111435245794]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_5 = train_target.loc[train_target.UNIQUENO == 201804081727258207]['SPN_FMI_Code'].value_counts() \n",
    "train_target_UNIQUENO_6 = train_target.loc[train_target.UNIQUENO == 201704111435245782]['SPN_FMI_Code'].value_counts()  \n",
    "\n",
    "train_target_UNIQUENO_1.plot(kind='bar',stacked=False,ax=ax[0,0])\n",
    "train_target_UNIQUENO_2.plot(kind='bar',stacked=False,ax=ax[0,1])\n",
    "train_target_UNIQUENO_3.plot(kind='bar',stacked=False,ax=ax[0,2])\n",
    "train_target_UNIQUENO_4.plot(kind='bar',stacked=False,ax=ax[1,0])\n",
    "train_target_UNIQUENO_5.plot(kind='bar',stacked=False,ax=ax[1,1])\n",
    "train_target_UNIQUENO_6.plot(kind='bar',stacked=False,ax=ax[1,2])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACCRUNTIMES</th>\n",
       "      <th>AREA</th>\n",
       "      <th>CITY</th>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th>FAULTTIME</th>\n",
       "      <th>FMI</th>\n",
       "      <th>FRID</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LNG</th>\n",
       "      <th>PROVINCE</th>\n",
       "      <th>READFLAG</th>\n",
       "      <th>SPN</th>\n",
       "      <th>UNIQUENO</th>\n",
       "      <th>SPN_FMI</th>\n",
       "      <th>SPN_FMI_Code</th>\n",
       "      <th>FRID_Top5</th>\n",
       "      <th>UNIQUENO_LAST4</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23150</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:23:45</td>\n",
       "      <td>2018-04-01 06:23:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357817.0</td>\n",
       "      <td>34.108867</td>\n",
       "      <td>34.108867</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23178</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:25:45</td>\n",
       "      <td>2018-04-01 06:25:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357818.0</td>\n",
       "      <td>34.108877</td>\n",
       "      <td>34.108877</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23208</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:27:45</td>\n",
       "      <td>2018-04-01 06:27:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357819.0</td>\n",
       "      <td>34.108891</td>\n",
       "      <td>34.108891</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23240</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:29:45</td>\n",
       "      <td>2018-04-01 06:29:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357821.0</td>\n",
       "      <td>34.109057</td>\n",
       "      <td>34.109057</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>23274</th>\n",
       "      <td>979.0</td>\n",
       "      <td>341322.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>2018-04-01 06:31:45</td>\n",
       "      <td>2018-04-01 06:31:45</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2357822.0</td>\n",
       "      <td>34.109043</td>\n",
       "      <td>34.109043</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8809.0</td>\n",
       "      <td>201704140835245913</td>\n",
       "      <td>8809_15</td>\n",
       "      <td>1206</td>\n",
       "      <td>23578</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       ACCRUNTIMES      AREA      CITY         COLLECTTIME  \\\n",
       "23150        979.0  341322.0  341300.0 2018-04-01 06:23:45   \n",
       "23178        979.0  341322.0  341300.0 2018-04-01 06:25:45   \n",
       "23208        979.0  341322.0  341300.0 2018-04-01 06:27:45   \n",
       "23240        979.0  341322.0  341300.0 2018-04-01 06:29:45   \n",
       "23274        979.0  341322.0  341300.0 2018-04-01 06:31:45   \n",
       "\n",
       "                 FAULTTIME   FMI       FRID        LAT        LNG  PROVINCE  \\\n",
       "23150  2018-04-01 06:23:45  15.0  2357817.0  34.108867  34.108867  340000.0   \n",
       "23178  2018-04-01 06:25:45  15.0  2357818.0  34.108877  34.108877  340000.0   \n",
       "23208  2018-04-01 06:27:45  15.0  2357819.0  34.108891  34.108891  340000.0   \n",
       "23240  2018-04-01 06:29:45  15.0  2357821.0  34.109057  34.109057  340000.0   \n",
       "23274  2018-04-01 06:31:45  15.0  2357822.0  34.109043  34.109043  340000.0   \n",
       "\n",
       "       READFLAG     SPN            UNIQUENO  SPN_FMI  SPN_FMI_Code  FRID_Top5  \\\n",
       "23150       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23178       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23208       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23240       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "23274       1.0  8809.0  201704140835245913  8809_15          1206      23578   \n",
       "\n",
       "       UNIQUENO_LAST4  Day  Hour  Minute  \n",
       "23150             591    1     6      23  \n",
       "23178             591    1     6      25  \n",
       "23208             591    1     6      27  \n",
       "23240             591    1     6      29  \n",
       "23274             591    1     6      31  "
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************************************************************** \n",
      " Min time in train dataset is: 201607080830045784  Max time in train dataset is: 201804302129198903\n",
      "**************************************************************************************************** \n",
      " Min time in test dataset is: 201607080830045784  Max time in test dataset is: 201805081928459051\n"
     ]
    }
   ],
   "source": [
    "print('*'*100,'\\n', 'Min time in train dataset is:',train_target['UNIQUENO'].min(), ' Max time in train dataset is:',train_target['UNIQUENO'].max())\n",
    "print('*'*100,'\\n', 'Min time in test dataset is:',test['UNIQUENO'].min(), ' Max time in test dataset is:',test['UNIQUENO'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有大约93.9627%的UNIQUENO出现在测试集中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are about 0.939672 data in test data have the same UNIQUENO in train data set\n"
     ]
    }
   ],
   "source": [
    "uniquenos = train_target['UNIQUENO'].unique() \n",
    "print('There are about %f data in test data have the same UNIQUENO in train data set' % (test.loc[test.UNIQUENO.isin(uniquenos)].shape[0] / test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 小结\n",
    "\n",
    "我们发现:通过细心的eda我们可以将一个100多维特征的数据转换为20维度不到的数据,再通过单元变量的分析(变量与变量,变量与标签)可以得到每个变量之间以及变量与标签之间的关系,从而可以初步判断出每个单元变量的重要性。此外,通过分析,我们还发现有一些强相关的特征能帮我们直接拿到测试集的标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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